This invention relates generally to the analysis of motor unit action potentials as used in diagnosing neuromuscular disorders, and more particularly the invention relates to an improved method and apparatus for processing a recorded electromyogram for identifying and measuring individual motor unit action potentials from the electromyographic interference pattern.
Electromyography, as used clinically to diagnose neuromuscular disorders, involves recording an electrical signal (the electromyogram or EMG) from a contracting muscle by means of a needle electrode.
The physiological origin of the EMG is fairly well understood. The neuromuscular system is organized in subunits called motor units, each of which consists of a single motorneuron and the set of muscle fibers it controls. When a motorneuron fires, it triggers a discharge of electrical impulses in the muscle fibers it innervates, and these in turn activate the muscle fibers' contractile apparatus leading to the generation of mechanical force. The net volume-conducted potential recorded by an intramuscular electrode during such a discharge is called the motor-unit action potential (MUAP). The strength of the muscular contraction is determined by the number of motor units activated and their firing rates. The motorneurons of a given muscle are thought to have fixed activation thresholds and thus to be recruited in a fixed order. During weak contractions individual MUAP trains stand out clearly in the MUAP. During more forceful contractions the MUAPS of many independently firing motor units overlap and form an interference pattern. (IP).
Interpretation of the electromyogram recorded using a needle electrode from a voluntarily contracted muscle is an important part of the clinical neurophysiological evaluation of patients suspected of having certain neuromuscular disorders. In current practice, this interpretation is most often a subjective and quantitative assessment, using oscilloscope and loudspeaker, of the sizes, shapes, and firing rates of the MUAPS recorded during weak contractions, and of the size and complexity of the interference pattern recorded during stronger contractions.
Richfield et al. "Review of Quantitative and Automated Needle Electromyographic Analyses", IEEE Transactions on Biomedical Engineering, Vol. BME-28, No. 7, July 1981, pages 506-514 review various methods which have been proposed for quantitating EMG analysis in order to make it more objective, reproducible, and diagnostically sensitive. The most highly regarded method involves measuring the amplitudes, durations, and numbers of phases of individual MUAPs recorded from several sites in the muscle. These measurements are commonly made by hand from photographic traces, although several semiautomatic techniques have been developed. The major shortcoming of this method is that it is restricted to low-force contractions and hence to early-recruited MUAPs. The IP recorded during more forceful contractions has, because of its complexity, by and large defied resolution into its component MUAPs. Proposed methods for quantitating the IP have instead concentrated on characterizing it statistically--e.g. in terms of its rate of zero crossings or turns, or its power spectral density. Unfortunately, these statistical characterizations have proven to be less reliable and less diagnostically sensitive than measurements of individual MUAPs.
Le Fever et al., "The Procedure for Decomposing the Myoelectric Signal Into its Constituent Action Potentials--Part 1: Technique, Theory, and Implementation", IEEE Transactions on Biomedical Engineering, Vol. BMW-29, No. 3, March 1982, pages 149-157 discuss a semiautomated technique for the decomposition or separation of a myoelectric signal into its constituent MUAP trains. The technique consists of a multi-channel myoelectric signal recording procedure, a data compression algorithm, a digital filtering algorithm and a hybrid visual-computer decomposition scheme. As described by Le Fever et al., the recorded signals are sampled at a rate several times higher than the Nyquist frequency and conditioned by a highpass filter. Le Fever et al. state that the sampling rate must be sufficiently high to reduce alignment errors and that sampling at a lower rate would produce poor results due to excessive alignment errors.
All methods for characterizing the interference pattern heretofore known, both manual and automated, share a fundamental limitation in imprecise measurement of the MUAP parameters due to the complicated nature of the interference pattern. Subtle disorders in subpopulations of motor units tend to be masked, often necessitating many needle insertions for adequate sampling, and resulting in measurements whose range of normal tends to be broad and overlap the ranges of mild disorders.